On a warm Connecticut evening in 1989, the two coauthors of this volume discussed the lack of books that explain computer modeling methods, in particular nonlinear regression analysis, to chemists and biochemists in an understandable way. Our own early experiences in this field required digging out information from very good but mathematically dense texts. Thus, we felt that there was a need for a monograph introducing the practical aspects of nonlinear regression analysis to the average chemist and biochemist.
This book is the result of our discussions. We attempt to provide a solid introduction to the basics of nonlinear regression analysis in a format that does not rely heavily on mathematical symbolism. Only a rudimentary knowledge of algebra, elementary calculus, statistics, and computers should be necessary for the reader. The book is suitable for senior undergraduates and graduate students in chemistry or biochemistry, as well as professional practitioners in these fields. Part of the material has been taught to senior chemistry majors and graduate students in courses on computers in analytical chemistry for 15 years.
For those with more advanced mathematical skills, ample references to source statistical literature are given in Chapters 1 to 4. These chapters provide a general introduction to the subject. Near the end of Chapter 2, we list current sources of software for nonlinear regression.
We present detailed models, examples, and discussions of applications to specific experimental techniques in Chapters 5 to 14. They include Potentiometrie titrations, macromolecular equilibria, X-ray scattering, ultracen-trifugation, electroanalytical methods, and spectroscopic and chromatographic analyses. In most cases, it should be relatively easy for readers to adapt the models in these chapters to their specific applications. We hope that this book fulfills our goal of enabling new or tentative users to model their experiments as painlessly as possible.
James F. Rusling Thomas F. Kumosinski
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